Studying identification algorithms in food technology

Abstract

Food production specificity requires improvement of systems of automatic regulation of the processes in aggregates, apparatus, and facility. In creating an adaptive process control system for food production, based on the model of a control object, an additional analysis should be done to choose the identification algorithm of a real, fairly representative sample of input data and output measurement results. By now a lot of recurrent identification algorithms are development and in one or another extent investigated. The little part of these algorithms is implemented into software, such as Matlab, Unity Pro, and other similar ones. But the problem is that among all of these algorithms the best one for real conditions of identification is absent. And a practicing engineer, who takes up the implementation of an adaptive control system with an identifier, is faced with the problem of choosing a specific identification algorithm.In this research, by using simulation modelling, over 53 recurrent identification algorithms were analyzed, plus the main modifications of these algorithms, and a total of 47 possible estimation criteria for non-stationary multidimensional dynamic objects. Based on this analysis of the object class in question, several algorithms were recommended to engineering practice. Possibilities of a software package that has the most complete set of parametric identification algorithms today are discussed. For these specific conditions of the identification algorithms comparison into the package by identifying the stationary coefficients into object equation, the most effective were: Aizerman-1, Kachmazh, Nagumo-Noda, Rastrigin, Kalman Filter, Forgetting Factor, Tsypkin. With non-stationary coefficients of the object -Kachmazh, Nagumo-Noda, Kalman Filter. The best result was shown by the Nagumo-Noda identification algorithm.

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